发表机构
University of Liège; Lund University, Skane University Hospital; Guizhou University; Maastricht University; Lund University; Aix-Marseille Univ, CNRS, CRMBM（列日大学; 隆德大学，斯卡纳大学医院; 贵州大学; 马斯特里赫特大学; 隆德大学; 艾克斯-马赛大学，法国国家科学研究中心，CRMBM）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TractSpLearn提出一种共享流形学习框架，联合健康对照与患者数据，实现轻度脑外伤中细微白质改变的个体化检测，并在多个队列中优于TractLearn。
AI 中文摘要
创伤性脑损伤（TBI）常导致细微的白质损伤，这些损伤在常规MRI上难以察觉。扩散峰度成像（DKI）作为扩散张量成像（DTI）的扩展，提供了关于非高斯水扩散的补充信息，对复杂的白质微结构敏感。随着超高场MRI的出现，空间分辨率和信噪比（SNR）显著提高，使得细微异常的更精确可视化成为可能。基于这些进展，我们开发了TractSpLearn，一种个体化的基于纤维束的学习框架，该框架同时考虑组内变异性和组间差异。与原始的TractLearn框架（仅从健康对照中学习规范流形）不同，TractSpLearn纳入健康对照和患者，以学习具有健康锚定表示和额外患者相关组件的共享流形。为评估所提出方法的性能，我们将TractSpLearn与原始TractLearn在三个队列中进行了比较：（i）健康对照（HC），（ii）患有持续性脑震荡后综合征（PPCS）的运动员，以及（iii）有反复头部损伤（RHI）的运动员，异常在轴向峰度（AK）和平均扩散率（MD）中尤为明显。在RHI中，TractSpLearn突出了患者间的复发性异常。在PPCS队列中，整体组间差异较为温和，可能反映了小样本量导致的有限统计功效以及恢复过程中白质改变的部分正常化。尽管如此，TractSpLearn在更多患者和更多受影响纤维束中识别出异常证据，优于TractLearn。
英文摘要
Traumatic brain injury (TBI) often leads to subtle white matter damage that remains undetected on conventional MRI. Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), provides complementary information on non-Gaussian water diffusion and is sensitive to complex white-matter microstructure. With the advent of ultra-high-field MRI, the spatial resolution and signal-to-noise ratios (SNR) have been significantly enhanced, enabling more precise visualization of subtle abnormalities. Building on these advances, we developed TractSpLearn, an individualized tract-based learning framework that jointly considers within-group variability and between-group differences. Unlike the original TractLearn framework, which learns a normative manifold exclusively from healthy controls, TractSpLearn incorporates both healthy controls and patients to learn a shared manifold with a healthy-anchored representation and an additional patient-related component. To assess the performance of the proposed method, we compared TractSpLearn with the original TractLearn in three cohorts: (i) healthy controls (HC), (ii) athletes with persistent post-concussive syndromes (PPCS), and (iii) athletes with repeated head injuries (RHI), with abnormalities particularly evident in axial kurtosis (AK) and mean diffusivity (MD). In RHI, TractSpLearn highlighted recurrent abnormalities across patients. In the PPCS cohort, the overall group-level differences were more modest, potentially reflecting both limited statistical power due to the small sample size and partial normalization of white-matter alterations during recovery. Still TractSpLearn identified abnormality evidence in more patients and across more affected tracts than TractLearn.
CommentsAli Al-Husseini and Yi Chen contributed equally as second authors. Niklas Marklund, Christophe Phillips and Evgenios N. Kornaropoulos contributed equally as last authors
